AI Agents Track

Build intelligent agents that can reason, plan, and take actions autonomously! This track teaches you everything from understanding what agents are to building production-ready agent systems.

What Are AI Agents?

AI agents are autonomous systems that can:

• Perceive: Understand context and gather information
• Reason: Think through problems step-by-step
• Plan: Break down complex tasks into actionable steps
• Act: Execute actions using tools and APIs
• Learn: Improve based on feedback and outcomes

Unlike simple chatbots that just respond to prompts, agents can work independently on multi-step tasks, make decisions, and use tools to accomplish goals.

Why Agents Matter

Agents represent the next evolution of AI capabilities:

• Automation at scale: Handle complex workflows end-to-end
• 24/7 operation: Work continuously without human oversight
• Consistent quality: Follow defined processes reliably
• Scalability: Handle many tasks simultaneously
• Integration: Connect to your existing tools and systems

What You'll Learn

1. Agent Fundamentals

• How agents differ from simple LLM calls
• The perception-reasoning-action loop
• Tool use and function calling
• Memory and context management

2. Building Agents

• Designing agent architectures
• Implementing tool systems
• Managing agent state
• Error handling and recovery

3. Production Agents

• Deployment strategies
• Monitoring and observability
• Safety and guardrails
• Scaling considerations

4. Agent Products

• Building products powered by agents
• User experience design
• Business model considerations
• Real-world case studies

Prerequisites

• Completed Start Here track
• Basic Python knowledge
• Familiarity with APIs
• Understanding of LLMs (Claude, GPT, etc.)

Time Required

8-10 hours to complete all sections

Agent Types

Task-Specific Agents

Agents designed for particular domains:

• Code Agent: Write, review, and debug code
• Research Agent: Gather and synthesize information
• Data Agent: Analyze datasets and generate insights
• Support Agent: Handle customer inquiries
• Content Agent: Create and edit content

General-Purpose Agents

Flexible agents that can handle various tasks:

• Personal Assistant: Manage tasks, calendar, emails
• Project Manager: Coordinate workflows and tasks
• Problem Solver: Tackle open-ended challenges

Multi-Agent Systems

Multiple agents working together:

• Collaborative: Agents with different expertise
• Competitive: Agents that debate or verify
• Hierarchical: Manager agents directing worker agents

The Agent Loop

At its core, every agent follows this pattern:

11. OBSERVE   - Gather context and information
22. THINK     - Reason about what to do next
33. PLAN      - Break task into steps
44. ACT       - Execute using tools
55. REFLECT   - Evaluate results
66. REPEAT    - Continue until goal achieved

Example: Research Agent

1# Pseudocode for a research agent
2 
3while not research_complete:
4    # OBSERVE
5    query = understand_user_question()
6 
7    # THINK
8    information_needed = identify_knowledge_gaps()
9 
10    # PLAN
11    search_queries = generate_search_plan()
12 
13    # ACT
14    for query in search_queries:
15        results = web_search(query)
16        notes = extract_key_information(results)
17 
18    # REFLECT
19    if sufficient_information():
20        research_complete = True
21        synthesize_findings()
22    else:
23        refine_search_strategy()

Tools: The Agent's Hands

Tools give agents the ability to interact with the world:

Common Tool Categories

| Category | Examples | |----------|----------| | Information | Web search, document reading, API calls | | Computation | Code execution, calculations, data analysis | | Creation | Writing files, generating images, sending messages | | Control | System commands, application control, device actions |

Tool Design Principles

1. Clear purpose: Each tool does one thing well
2. Defined inputs: Specific parameters with types
3. Predictable outputs: Consistent return format
4. Error handling: Graceful failure modes
5. Documentation: Clear descriptions for the agent

Memory: The Agent's Mind

Agents need memory to maintain context and learn:

Short-term Memory

• Current conversation
• Working context
• Immediate task state

Long-term Memory

• Past interactions
• Learned preferences
• Domain knowledge

Memory Strategies

• RAG (Retrieval): Search through stored information
• Summarization: Condense long contexts
• Key-value store: Quick access to facts
• Vector database: Semantic similarity search

Track Outline

Module 1: Building Agents

Learn to build agents from scratch with the Claude Agent SDK.

Topics covered:

• Agent architecture design
• Tool implementation
• State management
• Error handling

Module 2: Using Agents

Master effective agent usage and prompt engineering.

Topics covered:

• Writing effective prompts
• Tool selection strategies
• Output handling
• Debugging agents

Module 3: Agent Products

Build real products powered by autonomous agents.

Topics covered:

• Product architecture
• User experience design
• Deployment strategies
• Monetization models

Quick Start Example

Here's a simple agent that can search the web and answer questions:

Ask Claude Code:

1Create a simple research agent that:
21. Takes a question from the user
32. Searches the web for relevant information
43. Synthesizes the findings
54. Provides a comprehensive answer with sources
6 
7Use the Claude Agent SDK and include:
8- Web search tool
9- Note-taking for organizing information
10- Source citation

Expected Output

1from anthropic import Anthropic
2 
3client = Anthropic()
4 
5def research_agent(question: str) -> str:
6    """Simple research agent that searches and synthesizes."""
7 
8    tools = [
9        {
10            "name": "web_search",
11            "description": "Search the web for information",
12            "input_schema": {
13                "type": "object",
14                "properties": {
15                    "query": {"type": "string"}
16                },
17                "required": ["query"]
18            }
19        }
20    ]
21 
22    messages = [
23        {
24            "role": "user",
25            "content": f"""Research this question thoroughly: {question}
26 
27            Use the web_search tool to find relevant information.
28            Cite your sources in the final answer."""
29        }
30    ]
31 
32    # Agent loop
33    while True:
34        response = client.messages.create(
35            model="claude-sonnet-4-20250514",
36            max_tokens=4096,
37            tools=tools,
38            messages=messages
39        )
40 
41        # Check for tool use
42        if response.stop_reason == "tool_use":
43            # Execute tools and continue
44            tool_results = execute_tools(response.content)
45            messages.append({"role": "assistant", "content": response.content})
46            messages.append({"role": "user", "content": tool_results})
47        else:
48            # Agent completed
49            return extract_text(response.content)

Best Practices

Design Principles

1. Start simple: Begin with single-task agents
2. Add tools gradually: One capability at a time
3. Test extensively: Agents can behave unexpectedly
4. Monitor closely: Watch for loops and errors
5. Set boundaries: Define what agents cannot do

Common Pitfalls

• Infinite loops: Agent keeps trying same action
• Tool misuse: Wrong tool for the task
• Context overflow: Too much information
• Hallucination: Making up tool results
• Cost explosion: Uncontrolled API calls

Safety Considerations

• Always require human approval for sensitive actions
• Implement rate limits and cost caps
• Log all agent actions for audit
• Use sandboxed environments for testing
• Define clear boundaries and permissions

Real-World Applications

Software Development

• Code review automation
• Bug triage and fixing
• Documentation generation
• Test creation

Customer Support

• Ticket routing and response
• Knowledge base search
• Issue escalation
• Follow-up scheduling

Data Analysis

• Report generation
• Anomaly detection
• Trend analysis
• Visualization creation

Content Creation

• Blog post drafting
• Social media scheduling
• Email campaigns
• Translation workflows

Resources

Official Documentation

• Claude Agent SDK
• Tool Use Guide
• Claude API Reference

Learning Resources

• Building Effective Agents - Anthropic Research
• Agent Protocol - Standardization effort
• LangChain Agents - Popular framework

Community

• Discord: Claude Community
• GitHub: Agent Examples

Next Steps

Ready to start building agents? Begin with the first module:

Building Agents - Learn to architect and implement agents from scratch.

Or jump to specific topics:

• Using Agents - Master agent prompting and usage
• Agent Products - Build production agent systems

Let's build intelligent agents! Start with the fundamentals and work your way up to production-ready systems. Remember: the best agents start simple and evolve based on real-world feedback.